Background & AimsPreclinical identification of compounds at risk of causing drug induced liver injury (DILI) remains a significant challenge in drug development, highlighting a need for a predictive human system to study complicated DILI mechanism and susceptibility to individual drug. Here, we established a human liver organoid (HLO)–based screening model for analyzing DILI pathology at organoid resolution.MethodsWe first developed a reproducible method to generate HLO from storable foregut progenitors from pluripotent stem cell (PSC) lines with reproducible bile transport function. The qRT-PCR and single cell RNA-seq determined hepatocyte transcriptomic state in cells of HLO relative to primary hepatocytes. Histological and ultrastructural analyses were performed to evaluate micro-anatomical architecture. HLO based drug-induced liver injury assays were transformed into a 384 well based high-speed live imaging platform.ResultsHLO, generated from 10 different pluripotent stem cell lines, contain polarized immature hepatocytes with bile canaliculi-like architecture, establishing the unidirectional bile acid transport pathway. Single cell RNA-seq profiling identified diverse and zonal hepatocytic populations that in part emulate primary adult hepatocytes. The accumulation of fluorescent bile acid into organoid was impaired by CRISPR-Cas9–based gene editing and transporter inhibitor treatment with BSEP. Furthermore, we successfully developed an organoid based assay with multiplexed readouts measuring viability, cholestatic and/or mitochondrial toxicity with high predictive values for 238 marketed drugs at 4 different concentrations (Sensitivity: 88.7%, Specificity: 88.9%). LoT positively predicts genomic predisposition (CYP2C9∗2) for Bosentan-induced cholestasis.ConclusionsLiver organoid-based Toxicity screen (LoT) is a potential assay system for liver toxicology studies, facilitating compound optimization, mechanistic study, and precision medicine as well as drug screening applications. Preclinical identification of compounds at risk of causing drug induced liver injury (DILI) remains a significant challenge in drug development, highlighting a need for a predictive human system to study complicated DILI mechanism and susceptibility to individual drug. Here, we established a human liver organoid (HLO)–based screening model for analyzing DILI pathology at organoid resolution. We first developed a reproducible method to generate HLO from storable foregut progenitors from pluripotent stem cell (PSC) lines with reproducible bile transport function. The qRT-PCR and single cell RNA-seq determined hepatocyte transcriptomic state in cells of HLO relative to primary hepatocytes. Histological and ultrastructural analyses were performed to evaluate micro-anatomical architecture. HLO based drug-induced liver injury assays were transformed into a 384 well based high-speed live imaging platform. HLO, generated from 10 different pluripotent stem cell lines, contain polarized immature hepatocytes with bile canaliculi-like architecture, establishing the unidirectional bile acid transport pathway. Single cell RNA-seq profiling identified diverse and zonal hepatocytic populations that in part emulate primary adult hepatocytes. The accumulation of fluorescent bile acid into organoid was impaired by CRISPR-Cas9–based gene editing and transporter inhibitor treatment with BSEP. Furthermore, we successfully developed an organoid based assay with multiplexed readouts measuring viability, cholestatic and/or mitochondrial toxicity with high predictive values for 238 marketed drugs at 4 different concentrations (Sensitivity: 88.7%, Specificity: 88.9%). LoT positively predicts genomic predisposition (CYP2C9∗2) for Bosentan-induced cholestasis. Liver organoid-based Toxicity screen (LoT) is a potential assay system for liver toxicology studies, facilitating compound optimization, mechanistic study, and precision medicine as well as drug screening applications.

High-Fidelity Drug-Induced Liver Injury Screen Using Human Pluripotent Stem Cell–Derived Organoids

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